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After 10 years of trials and tribulations, stablecoins finally became the “peer-to-peer electronic cash” officially designated by the United States From geek technology to major asset classes, BTC paves the way for the future of stablecoins. What will be the crypto vision for the next decade?

Mihailo Bjelic, co-founder of Polygon, has announced his decision to step down from the board of Polygon Foundation. The Polygon ( POL ) co-founder revealed the move via a post on X on Friday, May 23, 2025, noting that he will also “wind down” his day-to-day involvement with Polygon Labs. “Polygon was born in 2019, and we’ve come a long way since then,” Bjelic wrote. “From significant breakthroughs in zero-knowledge tech, to onboarding some of the world’s biggest brands, we’ve made meaningful strides toward that grand vision. I’m proud of this, and grateful for the privilege to work with so many talented people.” Bjelic notes that he first entered the crypto space in 2013, with his foray into the ecosystem seeing “deep down the rabbit hole” by 2017. He co-founded Polygon , a layer-2 scaling solution on Ethereum ( ETH ) alongside Jaynti Kanani, Sandeep Nailwal and Anurag Arjun. The project, among the most searched proof-of-stake blockchain in the U.S. in 2023, rebranded from its initial name Matic Network. Explaining his decision further, Bjelic said: “As projects evolve and mature, it is natural for visions to evolve, and sometimes diverge. With this in mind, I can no longer contribute to Polygon to the best of my abilities.” While he will no longer be actively involved, he said he is confident Polygon will continue to succeed. Bjelic plans to offer his support “from the sidelines”. Bjelic’s exit from the Polygon Foundation board sees him join co-founders Jaynti Kanani and Anurag Arjun in stepping down from active roles at the blockchain project. Kanani left Polygon in October 2023.Meanwhile, Arjun left in March 2023 to focus on his modular blockchain project Avail . Nailwal said via X that words cannot describe what Bjelic means to him personally and to Polygon. “More than a co-founder, you’re a brother. From the earliest days — whiteboards full of ideas, endless whitepapers, governance frameworks, strategy calls deep into the night — you have been a force behind so much of what makes Polygon what it is today,” he added.

A research conducted by the Massachusetts Institute of Technology reveals a major flaw in artificial intelligence (AI): its inability to correctly grasp negation. This gap could have dramatic consequences in critical sectors such as healthcare. A research team led by Kumail Alhamoud, a PhD student at MIT, conducted this study in partnership with OpenAI and the University of Oxford. Their work reveals a disturbing flaw: the most advanced AI systems systematically fail when faced with negations. Renowned models like ChatGPT , Gemini, and Llama constantly favor positive associations, ignoring clearly explicit negation terms. The medical sector perfectly illustrates this issue. When a radiologist writes a report mentioning “no fracture” or “no enlargement”, AI risks misinterpreting this vital information. This confusion could lead to diagnostic errors with potentially fatal consequences for patients. The situation worsens with vision-language models, these hybrid systems that analyze images and texts together. These technologies show an even stronger bias towards positive terms. They often fail to differentiate between positive and negative descriptions, increasing the risk of errors in AI-assisted medical imaging. Franklin Delehelle, a research engineer at Lagrange Labs, explains that the heart of the problem does not lie in the lack of data. Current models excel at reproducing responses similar to their training, but struggle to generate truly new replies. Kian Katanforoosh, a professor at Stanford, explains that language models work by association, not by logical reasoning. When they encounter “not good”, they automatically associate “good” with a positive feeling, ignoring the negation. This approach creates subtle but critical errors, particularly dangerous in legal, medical, or human resources applications. Unlike humans, AI fails to overcome these automatic associations. Researchers are exploring promising avenues with synthetic negation data. However, Katanforoosh emphasizes that simply increasing training data is not sufficient. The solution lies in developing models capable of logical reasoning, combining statistical learning with structured thinking. This evolution represents the major challenge of modern artificial intelligence.

Worldcoin is spending $54 in tokens on every new user signup through biometric device orb. The user acquisition cost was supposed to be zero in my vision of Decentralised Identity, which I proposed in 2016. We had planned to use an existing network of visitor management

2 days ago, BTC made a new ATH. Today, HYPE now joins BTC as the only altcoin to also be doing price discovery. This is a testament to the quality of the project, the vision of what it can become, and the community that believes in it. Job's not finished though. Hyperliquid